GlidarCo: gait recognition by 3D skeleton estimation and biometric feature correction of flash lidar data

Abstract

Gait recognition using noninvasively acquired data has been attracting an increasing interest in the last decade. Among various modalities of data sources, it is experimentally found that the data involving skeletal representation are amenable for reliable feature compaction and fast processing. Model-based gait recognition methods that exploit features from a fitted model, like skeleton, are recognized for their view and scale-invariant properties. We propose a model-based gait recognition method, using sequences recorded by a single flash lidar. Existing state-of-the-art model-based approaches that exploit features from high quality skeletal data collected by Kinect and Mocap are limited to controlled laboratory environments. The performance of conventional research efforts is negatively affected by poor data quality. We address the problem of gait recognition under challenging scenarios, such as lower quality and noisy imaging process of lidar, that degrades the performance of state-of-the-art skeleton-based systems. We present GlidarCo to attain high accuracy on gait recognition under the described conditions. A filtering mechanism corrects faulty skeleton joint measurements, and robust statistics are integrated to conventional feature moments to encode the dynamic of the motion. As a comparison, length-based and vector-based features extracted from the noisy skeletons are investigated for outlier removal. Experimental results illustrate the efficacy of the proposed methodology in improving gait recognition given noisy low resolution lidar data.

Real-Time Identification from Gait Features Using Cascade Voting Method

“…The application of these methods is easier, and their computational complexities are smaller compared to model-based methods. However, modelfree methods depend on the angle and scale of the image taken and require recording with multiple cameras [8]. In addition, clothing of individuals also has an important effect on the gait information obtained.…”

“…Therefore, it is quite appropriate for the surveillance and security scenarios in shared areas often used by societies without harming the privacy of individuals while identifying an individual within the system. The combination of biometricbased static features and the features obtained from motion analysis of some joints may create an effective data set to identify an individual [8]. Many systems providing identification based on a gait pattern extract the silhouette of the individual from the image and perform the identification either by obtaining gait information from this silhouette or by the gait information obtained from the various joint points provided by the emerging model as a result of the matching of these silhouettes to a human skeleton model.…”

“…For the gait information of individuals to be extracted, silhouettes are obtained by separating the background and the individuals. Model-free approaches can be applied easily but they are not fixed-scaled and require recording from various angles [8]. Model-based approaches, on the other hand, aim at identifying by matching a skeleton model to the individual whose image is taken [9].…”